Enhancing On-Board Orbit Prediction with Machine Learning

In recent years, the demand for autonomous spacecraft operations has grown significantly, becoming a critical factor across all areas of space engineering. Accurate onboard orbit determination (OD) and orbit prediction (OP) are essential for reliable autonomous mission planning. However, onboard orbit modeling remains challenging due to inherent uncertainties and environmental complexity. This paper presents a hybrid approach combining traditional physics-based OD with a machine learning (ML) method inspired by Peng and Bai [1]. A lightweight Artificial Neural Network (ANN) is implemented for real-time onboard OP error correction. Simulations demonstrate the method’s effectiveness using realistic GPS tracking data generated by a high-fidelity model. Results show significant improvements in OP accuracy, reducing maximum along-track errors from approximately 5 km to about 1.2 km over a three-day OP horizon. The hybrid method is planned to be experimentally validated onboard the ATHENE-1 satellite [2], scheduled for launch in 2026, demonstrating enhanced spacecraft autonomy.